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Abstract

Timely and accurate bearing fault detection and diagnosis is important for reliable and safe operation of industrial systems. In this study, performance of a generic real-time induction bearing fault diagnosis system employing compact adaptive 1D Convolutional Neural Network (CNN) classifier is extensively studied. In the literature, although many studies have developed highly accurate algorithms for detecting bearing faults, their results have generally been limited to relatively small train/test data sets. As opposed to conventional intelligent fault diagnosis systems that usually encapsulate feature extraction, feature selection and classification as distinct blocks, the proposed system takes directly raw time-series sensor data as input and it can efficiently learn optimal features with the proper training. The main advantages of the 1D CNN based approach are 1) its compact architecture configuration (rather than the complex deep architectures) which performs only 1D convolutions making it suitable for real-time fault detection and monitoring, 2) its cost effective and practical real-time hardware implementation, 3) its ability to work without any pre-determined transformation (such as FFT or DWT), hand-crafted feature extraction and feature selection, and 4) its capability to provide efficient training of the classifier with limited size of training data set and limited number of BP iterations. Effectiveness and feasibility of the 1D CNN based fault diagnosis method is validated by applying it to two commonly used benchmark real vibration data sets and comparing the results with the other competing intelligent fault diagnosis methods.